ABSTRACT
PURPOSE: The aim of this study was to analyze the characteristics of CT-measured intersection angle (FB-BNLD) between the frontal bone and bony nasolacrimal duct and to provide suggestions for treating primary acquired nasolacrimal duct obstruction (PANDO) patients in West China. METHODS: Three hundred and nine participants' CT were, respectively, evaluated with RadiAnt DICOM Viewer. We defined the FB-BNLD angle >0° as the anterior type and the FB-BNLD angle ≤0° as the posterior type. RESULTS: The mean FB-BNLD was -2.52° (95% CI, -3.16° to -1.88°) across all participants, of whom 37.2% were of the anterior type and 62.8% of the posterior type. Approximately 65.0% of the female patients had a posterior FB-BNLD type, and 54.2% of the male patients had an anterior FB-BNLD type (p = .002). Posterior FB-BNLD was the dominant type in the PANDO and control groups (p = .011), and the angle of FB-BNLD was statistically different in both groups (PANDO group, -2.54° to -0.71°; control group, -4.42° to -2.67°; p < .001). Among the male participants, the type of FB-BNLD differed between the two groups (p = .036), with differences in the angle of FB-BNLD (PANDO group, 0.59° to 5.13°; control group, -4.08° to 1.89°; p = .034). There was no difference in the type of FB-BNLD in female participants between the two groups (p = .051). CONCLUSION: The present study revealed individual differences in the type of FB-BNLD, with anterior-type majority in males and posterior-type dominance in females. Evaluating the FB-BNLD type on CT can provide a fast method for knowing the nasolacrimal duct condition during planning for lacrimal manipulation.
ABSTRACT
BACKGROUND: To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment. METHODS: A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening. RESULTS: The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening. CONCLUSIONS: In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.
